
where \(a_{uv} = 1\)  if there is an incident edge \((u,v) \in \mathcal {E}_t\)  and \(a_{uv} = 0\)  if \((u,v) \notin \mathcal {E}_t\)  and \(u,v \notin \mathcal {Q}_t\) ; \(r\)  is the so-called “margin” hyperparameter [1]}; \({\bf W}\)  indicates the learnable parameters of two MLPs that are shared with each other; and \(D_{\bf W}(\cdot ,\cdot )\)  is the distance function that measures the similarity between two input data and is expressed as
\(D_{\bf W}(u,v)= \cos ({\bf e}_u,{\bf e}_v),\) 
